ph.d. thesis presentation
DESCRIPTION
The slide includes animations. The source is available for download. Abstract: Research initiative in Service-Oriented Computing (SOC) aims at developing adaptable and scalable distributed applications and addressing challenges such as application integration, reusability, modularity, and interoperability. Service-Oriented Architecture (SOA) as an architectural style enables organizations to offer their application functionality as a service and enhance the adaptability to changes of new requirements of stakeholders, i.e., service consumers. Nowadays enterprises and service providers face several challenges to develop SOA-based solutions. They indispensably require to effectively manage variability in both functional and non-functional (quality) requirements at the business process level to rapidly and cost-effectively develop and deploy customized services that best meet the stakeholders' feature needs. SOAs provide the architectural underpinnings to support software reuse and enable variability at both design and run-time; however, they lack support to manage variability that promotes configurability and customization. Variability modeling and management have been the core research subjects in Software Product Line Engineering (SPLE) with the objective of addressing the issues of engineering and developing software-intensive systems. Combining SPLE and SOAs has been a subject of considerable research interest in recent years to develop highly configurable software systems. We adopted a product-line approach in the service domain and hypothesized that the SPLE paradigm, enabling variability management and systematic planned reuse, can be applied orthogonally to aid Service-Oriented Software Engineering (SOSE) to yield these benefits and construct Service-Oriented Software Product Lines (SOSPLs). We proposed the Configurable Process Models as the realization of SOSPLs, where services are the building blocks for the implementation of software features, which provide support for variation among members of a product line configured based on users' requirements. We are interested to provide automated decision-making support in the course of configuration helping to create tailored software services according to users’ preferences.TRANSCRIPT
Quality-aware Service-Oriented Software Product Lines: Feature-Driven Process Configuration and
Optimization
Bardia Mohabbati Simon Fraser University
Ontological Research Group
December 10, 2013
2
• Background
• Motivation
• Research Objectives
• Related work
• Approach Overview
• Evaluation
• Conclusions
• Future Work
Outline
3
Service-Oriented Architecture (SOA)Roles and Operations
Service Provider
Service Requester
Service Broker
Bind
PublishFind
• Enhancing architectural flexibility • Loose coupling among interacting software applications• Reusability of services• Interoperability
SLA SLA
Business-to-Business (B2B)
QoSQoS
+
+
+ +
…Party 1
Party 2 Party 3
Component Layer
Service Layer(Simple and Composite)
Process Layer(Orchestration & Choreography)
Consumer Layer(Presentation Layer)
4
Service-Oriented Architecture Layers
Package ApplicationsTechnologiesDBMS
Data Warehouses
Legacy Applications
QoS
QoS
SLA SLA
Business-to-Business (B2B)
QoS QoS
QoS QoS
QoSQoS QoSQoS
QoSPolicies
+
+
+
QoSPolicies
+
…Party 1
Party 2 Party 3
Operational Layer(Operational Systems)
Component Layer
Service Layer(Simple and Composite)
Process Layer(Orchestration & Choreography)
Variation Point
Optional
Dependency
Inte
grati
on L
ayer
Qua
lity
of S
ervi
ce (
QoS
) Lay
er
Info
rmati
on A
rchi
tect
ure
Lay
er
Consumer Layer(Presentation Layer)
Gov
erna
nce
Laye
r
QOS
QOS
Identity Management
Credit Cards
Smart Cards
Fraud Protection
Email Notification
Phone/Fax Notification
Mobile-based Notification
(MMS-SMS)
Debit Cards
E-Checks
Credit CardVerification
Logging
Payment Service
5
MotivationConfigurable Business Process Models
Stakeholder’sPreferences
Service Configuration
S4
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QOS
QOS
QOS
QOS
QOSVariable functional and quality
Payment Service
Payment Service
Payment Service
6
Complexity of Variability Management
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A
s323 s657 s323 s21 s987 s342 s126
Starts3
End
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s3
s2
s6
s5
s6
7
S2(1)
S2(2) . . .S2(23)
Sk(1)
Sk(2) . . .Sk(l)
S4(1)
S4(2) . . .S4(6)
S6(1)
S6(2)
S6(3)
S1(1)
S1(2) . . .S1(l)
Si(1)
Si(2) . . .Si(18)
Identity Federation
Credit Card
Payment
Debit Card Payment
Fraud Detection
Credit Card Number
Validation(Pre Verification) Mobile-based
Notification(MMS-SMS)
Phone/Fax Notification
Email/ Voice Mail
Selecting Payment
(Gateway Interface)
Notification ServicesPayment Services
Process Model
• QoS-aware Process Configuration
• Variability Modeling and Management
Execution Time
Price
Security
Availability
Research Objectives
• QoS Evaluation
Quality Ranges (quantitative/qualitative )
8
Related Work(Configurable) Business Process Models
• Business Process Modeling Notation (BPMN)
• Business Process Execution Language (BPEL)
• Yet Another Workflow Language (YAWL)
• Event-Driven Process Chains Language (EPC)
C-YAWL
C-EPC
UML ADs + BPMN
Puhlmann et al., (PESOA), 2005Razavian et al. 2008Reinhartz-Berger et al. (ADOM), 2009, 2010
van der Aalst et al. 2005Gottschalk et al. 2008
La Rosa et al. 2011Reijers et al. 2009
Rosemann et al. 2003Dreiling et al. 2005,2006
C-iEPC
C-aEPC
VxBPELKoning et al. 2005
Process Modeling Languages Extensions
9
Related workSoftware Product Line Engineering (SPLE)
Decision Modeling
● Atkinson et al. 2002 ● Schmid et al. 2004 ● Dhungana et al. 2010
Orthogonal Variability Modeling
● Pohl et al. 2005● Metzger et al. 2007
Feature Modeling
● Kang et al. ● Jacobson et al.● Griss et al. ● Kang et al. ● Czarnecki et al.● Hein et al.● Gurp et al. ● Riebisch et al.● Gomaa et al. ● Czarnecki et al.● Moon et al.
19901997199819982000200020012002200420052005
● Czarnecki et al.● Batory et al.● Sun et al.● Benavides et al.● Massen et al.● Wang et al.
● Batory et al.● Gheyi et al.● Zhang et al.● Benavides et al.● Trinidad et al.● Fan et al.
● Mannion et al. ● Massen et al.● Cao et al.
● Mendonca et al.● Trinidad et al.● White et al. ● Segura● Zhang et al.● Hemakumar● Gheyi et al.● Osman et al.● Osman et al.
● Mendonca et al. ● Thum et al.● Yan et al.● Salinesi et al.● White et al.● Abo Zaid et al.● Osman et al.● Fernandez et al.● Broek et al.● Favaro et al.
● Benavides et al.● Zhang et al.● Massen et al.● Storm et.al
Kang et al.(FODA)
Kang et al.(FORM) Deursen et al.
● Wang et al.● Storm et.al● Djebii et al.● Bachmeyer et al.
Curp et al.
Feature Model and Variability AnalysisSoftware product line engineering is a development paradigm to develop and maintain families of products while taking advantage of their common aspects and predicted variabilities – [Weiss and Lai 1999]
10
Variability Modeling
Feature Resolution(Mapping Schema)
Reference Business Process
Model
Reference Business Process
Model Implementation
Non-Functional Specifications
Product LineRequirements
Analysis
Requirements Models
Feature Model
Configurable
Business Process
Model
Service Discovery/ Implementation
Binding
Mapping Model
Feature Model enriched by Supporting
Quality RangesService-Domain
Implementation
Service-DomainDesign
Service-Domain Analysis
D1
D2
D3
D4
D5
D6
Feature Selection
Application Integration-
Deployment
Stakeholder’s Requirement
Analysis
Service-Application Requirement Specifications
Configured Feature Model
Service ProductService
Deployment
ServiceApplication
Design &
Implementation
ServiceApplication
AnalysisA1
A2
A3
ServiceSelection
ConfiguredReference
Business Process
Approach Overview
Service-Domain Engineering Service-Application Engineering
11
Approach Overview
ConfiguredService
Stakeholder‘sRequirements
Configuration
Configurationand
Integration
S2(1)
S2(2) . . .S2(20)
Application Requirement Analysis
Mapping
Domain Analysis( Variability Model)
Domain Design and
Implementation
...
S1(13)
...
S4(9)
S3(5)
...
Service-Domain Engineering Service-Application Engineering
Service
...
12
Variability Modeling & RepresentationService-Domain Engineering
Identity Federation
Credit Card
Payment
Debit Card Payment
Fraud Detection
Credit Card Pre-
Validation
Payment Gateway Interface
Shipment
Mobile-based Notification(MMS-SMS)
Phone/Fax Notification
Email-Voicemail
Notification
Credit CardPre-
ValidationDebit CardPayment
PaymentMethod
Advertising Management
Feature Model (FM)
Fraud Detection
Payment
Credit CardPayment
Email-Voicemail
PhoneFax
Mobile-basedNotification
Identity Federation
GatewayInterface
kf
f
if Shipment
Email Marketing
Mobile Marketing
Mobile e-Card
SMS MMS MobileCoupon
jf
...
...
Payment Service
Payment Method
USSD
Notification
4 k
Gat
eway
AND
OR
XOR
BP Notation
Activity
Workflow
And Or Alternative Optional Mandatory
Legend
Include
Exclude
Riq Quality range
Mappingn
2 3
Reference Process Model (BP)
Fraud Detection
Notification
Credit CardPre-
ValidationDebit CardPayment
PaymentMethod
Advertising Management
Feature Model (FM)
Payment
Credit CardPayment
Email-Voicemail
PhoneFax
Mobile-basedNotification
Identity Federation
GatewayInterface
kf
f
...if Shipment
Email Marketing
Mobile Marketing
Mobile e-Card
SMS MMS MobileCoupon
jf
...
...
USSD
And Or Alternative Optional Mandatory
Legend
Include
Exclude
Riq Quality range
Mapping
4 k
n
2 3
Mapping ModelService-Domain Engineering
Annotation-based approach (Czarnecki et al. 2005)
Reference Process Model (BP)
Feature Model (FM)
Identity Federation
Credit Card
Payment
Debit Card Payment
Fraud Detection
Credit Card Pre-
Validation
Payment Gateway Interface
Shipment
Mobile-based Notification(MMS-SMS)
Phone/Fax Notification
Email-Voicemail
Payment Method
Notification
Gat
eway
AND
OR
XOR
BP Notation
Activity
Workflow
Payment Service
i
2 , 5
2
2 , 4
2 , 3 , 1
3
2 , 1 2 , 2
2 , 3
2 , 3 , 2 2 , 3 , 3
2 , 5 , 1 2 , 5 , 2 2 , 5 , 3
2
2 , 1 2 , 2 2 , 3
2 , 4
2 , 5
2 , 5 , 1
2 , 5 , 2
2 , 5 , 3
3
2 , 3 , 1 2 , 3 , 2
2 , 3 , 3
14
Quality VariabilityService-Domain Engineering
Identity Federation
Credit Card
Payment
Debit Card Payment
Fraud Detection
Credit Card Number
Validation
Payment Gateway Interface
Mobile-based Notification(MMS-SMS)
Phone/Fax Notification
Email-Voicemail
[10 , 54]
[15 , 51]
[5 , 17]
[17 , 48]
S1(1)
S1(2) . . .S1(20)
Payment Method
[8 , 20]
Notification
[10 , 45]
[8 , 27]
[3 , 23]
[40ms , 270ms]Payment Service
Fraud Detection
Notification
Credit CardPre-
ValidationDebit CardPayment
PaymentMethod
Advertising ManagementPayment
Credit CardPayment
Email-Voicemail
PhoneFax
Mobile-basedNotification
Identity Federation
GatewayInterface
kf
f
Shipment
Email Marketing
Mobile Marketing
Mobile e-Card
SMS MMS MobileCoupon
jf
...
...
USSD
4 k
2 3
[40ms , 270ms]
Quality rangeExecution Time
S4(1)
S4(2) . . .S4(23)
S6(1)
S6(2) . . .S6(l)
S4(1)
S4(2) . . .S4(6)
S2(1)
S2(2) . . .S2(18)
[12 , 30]
15
Structural Variability and Composition Patterns
a1
an
a1
an
a1
an
Sequential Patterns
a1 an
an
CP1: Sequence CP2: Loop
Parallel Patterns
CP3: Parallel split – Synchronization (AND-AND)
CP4: Parallel split – Multi merge (AND-AND)
CP5: Parallel split – Discriminator (AND-DISC)
CP6: Parallel Split – Simple merg (AND-XOR)
CP7: Exclusive – Simple merg (XOR-XOR)
CP8: Multi-choice – Simple merg (OR-XOR)
CP9: Multi-Choice – Synchronization (OR-OR)
CP10:Multi-Choice – Multi merge (OR-OR)
CP11: Multi-choice – Discriminator (OR-DISC)
Variability Patterns Composition Patterns
a1
an
a1
an
a1
an
a1
an
a1
an
a1
an
...
k k
...
f
1f 2f
nf
1b
bn
bn
bn
bn
bn b
n bn
bn b
n
f
1f nf1a na
1a na 2a
...
Alternative-group group
k k
f
1f 2f
nf1a na 2a
Or-group
ai
AND XOR OR DISC (m/n) Activity Legend
...
Van Der Aalst et al. 2003
16
Service-Application Engineering
ConfiguredService
Stakeholder‘sRequirements
Mapping Configuration
Domain Analysis( Variability Model)
Domain Design and
Implementation
Configurationand
Integration
S2(1)
S2(2) . . .S2(20)
Application Requirement Analysis
...
S1(13)
...
S4(9)
S3(5)
...
Service-Domain Engineering Service-Application Engineering
Service
17
Configuration RequirementsService-Application Engineering
1. Functional properties
2. Non-functional properties
3. User’s preferences
4. Optimization
f4
f5
f6
f3f2
S4(1)
S4(2) . . .S4 (9)
S3(1)
S3(2) . . .S3(6)
S6(1)
S6(2) . . .S6(7)
S2(1)
S2(2) . . .S2(20)
S5(1)
S5(2) . . .S5(12)
1f
18
Configuration ProblemService-Application Engineering
Configuration Problem: How to find an optimal decision that selects the right set of features and service implementations based on constraints defined in system and user’s preference about QoS?
3f
4f 5f 6f
f
1f 2f
f4f3
S3(1)
S3(2) . . .S3(6)
Sj(1)
Sj(2) . . .Sj (6)
f5
f7
f3f2
Sj(1)
Sj(2) . . .Sj (6)
S1(1)
S1(2) . . .S1(20)
Sk(1)
Sk(2) . . .Sk(l)
S1(1)
S1(2) . . .S1(20)
Price
Execution Time
Security
Availability
QoS
Price
Execution Time
Security
Availability
High
Low
Prio
rity
19
Configuration FrameworkQoS-Aware Optimization and Configuration
ServiceRepository
Tailored Service
Fea
ture
S
elec
tio
n
Mapping
Service Selection
PreferencePrioritization
User’sPreference
Reference Process Model
Process ModelAnalyzer
Service Broker
Feature Model Analyzer
Process ModelConfigurator
Service Binder
Optimization Model
Optimizer Engine
123
Feature Model
20
Conditional PreferenceService-Application Engineering
Conditional Stratified AHP: Analytical Hierarchy Process (AHP) [Satty, 1981]
QoS
Price
Security
Throughput
Execution Time
Price. Low ≻ Security. High , Execution Time. Medium ≻ Throughput . Low α8 α2
Execution Time. Low ≻ Price. Low , Throughput . High ≻ Price. Low α9 α3
Security. High ≻ Price. High , Execution Time. Medium ≻ Security. Low , Execution ≻ Throughputα3 α7 α1
Degree of importance
α1
α2 α3
α4
α5
α6
α7
α8
α9
Num
eric
al S
cale
: Equal importance : Weak importance: Moderate importance : Moderate plus: Essential or strong importance: Strong plus: Very strong importance : Very, very strong importance: Extreme importance
The outcomes of the procedure are the QoS requirements ranked according to user’s preference.
These rankings are used as the main instrument for measuring the levelof satisfaction of user’s requirements with a particular configuration.
W1 W≻ 2 W≻ 3 W≻ 4
W1
W2
W3
W4
21
We model and formulate the configuration problem as a Mixed-Integer Linear Programming (MILP) model which is characterized by four constituents:
•A set of decision variables (X)•Domain of variables (D={0,1})•A set of constraints (C)•Objective function (U)
The output of the MILP problem is the maximum (or minimum) value of the objective function (U) and the values of variables at this maximum/minimum.
QoS-Aware Optimization and ConfigurationService-Application Engineering
Constraint Optimization Problem (COP)
f2 f3 f4 f5
fr
f1
f6 f7 f8 f9 f10 f11
x5
f13f12
f14 f15 f18 f19
1 1
1 3
1 2
1 1
x6
x1 x2 x4x3
x7 x8 x9 x10 x11
x12 x13
x0
x14 x15 x18 x19
22
Formalizing Feature Model in MILP ModelOptimization Model
fC
fp fp
fC1 fC2 fCn
1 n
...
fjfifjfi
fp
fC1 fC2 fCn
1 1
...fC
fp
23
Service Assignment and Dependency ConstraintsOptimization Model
S1(1)
S1(2) . . .S1(20)
S3(1)
S3(2) . . .S3(v)
S2(1)
S2(2)
S2(3)
Sj(1)
Sj(2) . . .Sj (6)
Sn(1)
Sn(2) . . .Sn(m)
Sk(1)
Sk(2) . . .Sk(l)
f1
f2
f3
fj
fk
fn
…
….
a1
a2
a3
aj
ak
an
a 2
a 1 a j
a 3
a k
a n
Service Assignment
Service Dependency
f1
f2
f3
fj
fk
fn
Execution time (ms)Throughput (Invocation/Sec.)Cost (Unit per invocation)
24
Global & Local Quality ConstraintsOptimization Model
The overall process model for a particular service application can be subjected to m global QoS constraints as follows:
S3(1)
S3(2) . . .S3(v)
…
….
a1
a2
a3
aj
ak
an
S2(1)
S2(2)
S2(3)
a 2
S1(1)
S1(2) . . .S1(20)
a 1
Sj(1)
Sj(2) . . .Sj (6)
a j
a 3
Sk(1)
Sk(2) . . .Sk(l)
a k
Sn(1)
Sn(2) . . .Sn(m)
a n
Local Constraints
…Execution time (ms)
Global Constraints
…
25
Optimization ModelQoS-Aware Optimization and Configuration
We formulate the problem of finding the optimal configuration of the process model as a maximization of objective function, which meets all the constraints specified in the model
FAMA BS
26
Evaluation & Analysis of ApproachMethodology and Experimental Framework
TransformationsProcess ModelGenerator
Service Generator
QoS Generator
Optimizer Engine
Feature Model Generator
Preference GeneratorReader/Writer
s70
s42
s33
s89
s47
s54
s67
s78
s34s43
s93
s29
s67
RefractionStart
s421
s25
s34
s54
s976
s79
s435 s43
s567
s56
s86
s39
s26
s543
s19 s87
s73
s71
s79
s70
s42
s33
s89
s47
s54
s67
s78
s34s43
s93
s29
s67
RefractionStart
s421
s25
s34
s54
s976
s79
s435 s43
s567
s56
s86
s39
s26
s543
s19 s87
s73
s71
s79
s70
s42
s33
s89
s47
s54
s67
s78
s34s43
s93
s29
s67
RefractionStart
s421
s25
s34
s54
s976
s79
s435 s43
s567
s56
s86
s39
s26
s543
s19 s87
s73
s71
s79
s65
s44
s345 s57
s876
s43 s56
s99 s765
27
Evaluation & Analysis of ConfigurationPerformance and Scalability
ExperimentNo.
ServiceActivity No.
(na)
ServiceCandidates No.
(ns)
Process Model (PM) Variability Model (FM)QoS No.
(nq)Percentage Ratio (RCP) Percentage Ratio (RVP)
(I) (II) (III)
500 600 1000
[2,4, 8,..., 128]
Sequence (SEQ)Parallel (AND)Multiple Choice (OR)Exclusive Choice (XOR)
25%25%25%25%
MandatoryOptionalityOr-groupXor-groupIntegrity Constraints
25%25%25%25%18%
5
Intel Xeon Dual CPU 2.8 GHz processor with 8 GB of memory, IBM ILOG Cplex 12.1
28
Evaluation & Analysis of QoS aggregationQoS-range Evaluation
29
Impact Analysis of Variability and Composition Patterns on Computational Cost
VariabilityPatterns
CompositionPatterns
Variability models: N= 4Variability pattern ratios: 25%, 50%, 75%, 100%
30
Impact Analysis of Structural Variability Patterns on Computational Cost
(e)
Process models: N= 100Activity : na = 300Service : ns = 128
Between variability patterns
Optional
Mandatory
Or-group
Xor-group
Integrity Constraints
1 2
43
5
1
2
3
4
5
31
Impact Analysis of Composition Patterns on Computational Cost
(a) (b)
(c) (d)
Process models: N= 100Activity : na = 300Service : ns = 128
Between composition patterns
Sequential
Parallel-AND
Parallel-OR
Parallel-OX
1
2
3
4
1 2
43
Composition pattern ratios: 25%, 50%, 75%, 100%
32
• State-of-the-art analysis− A systematic mapping study
• A method for design and development of configurable process models− Feature-oriented approach (modeling and managing variability of functional and quality properties)
• QoS model and evaluation method− An extensible multidimensional QoS model
− Quality-range aggregation and computation
• QoS-aware business process configuration framework− Preference-based configuration and optimization
− Automated decision support of variants in business process models
ConclusionsContributions
33
• Configuration and Customization Validation
• Quality Management and Probabilistic Evaluation
• Design & Run-time Variability Management
Future Work
34
Thank you